What is INVIYA Spandex Business | Indorama India Private Limited's cybersecurity risk score?

INVIYA Spandex Business | Indorama India Private Limited has an AI-generated cybersecurity risk score of 756 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has INVIYA Spandex Business | Indorama India Private Limited experienced?

According to Rankiteo, INVIYA Spandex Business | Indorama India Private Limited currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has INVIYA Spandex Business | Indorama India Private Limited been affected by any supply chain cyber incidents ?

According to Rankiteo, INVIYA Spandex Business | Indorama India Private Limited has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does INVIYA Spandex Business | Indorama India Private Limited have SOC 2 Type 2 certification ?

According to Rankiteo, INVIYA Spandex Business | Indorama India Private Limited does not hold a SOC 2 Type 2 certification.

Does INVIYA Spandex Business | Indorama India Private Limited have PCI DSS certification ?

According to Rankiteo, INVIYA Spandex Business | Indorama India Private Limited does not currently maintain PCI DSS compliance.

Does INVIYA Spandex Business | Indorama India Private Limited have ISO 27001 certification ?

According to Rankiteo,INVIYA Spandex Business | Indorama India Private Limited is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

How many employees does INVIYA Spandex Business | Indorama India Private Limited have ?

INVIYA Spandex Business | Indorama India Private Limited employs approximately 286 people worldwide.

Does INVIYA Spandex Business | Indorama India Private Limited own any subsidiaries ?

INVIYA Spandex Business | Indorama India Private Limited presently has no subsidiaries across any sectors.

How many LinkedIn followers does INVIYA Spandex Business | Indorama India Private Limited have ?

INVIYA Spandex Business | Indorama India Private Limited's official LinkedIn profile has approximately 11,251 followers.

What is the NAICS classification code for INVIYA Spandex Business | Indorama India Private Limited ?

INVIYA Spandex Business | Indorama India Private Limited is classified under the NAICS code 313, which corresponds to Textile Mills.

Does INVIYA Spandex Business | Indorama India Private Limited have a Crunchbase profile ?

No, INVIYA Spandex Business | Indorama India Private Limited does not have a profile on Crunchbase.

Does INVIYA Spandex Business | Indorama India Private Limited have a LinkedIn profile ?

Yes, INVIYA Spandex Business | Indorama India Private Limited maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/inviya.

How many peer or competitor companies does INVIYA Spandex Business | Indorama India Private Limited have ?

INVIYA Spandex Business | Indorama India Private Limited has an estimated 2,206 peer or competitor companies worldwide.

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ISBIIPL

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756

/1000

Fair

781

/1000

Fair

INVIYA Spandex Business | Indorama India Private Limited Vendor Cyber Rating & Cyber Score

inviya.com

Add Your Compliance Badge

Indorama India Private Ltd. is a part of the multinational conglomerate, Indorama Corporation, led by Mr. SP Lohia (Chairman) which is headquartered in Singapore. With increasing opportunities offered by the textile, clothing and organized retail industry, Indorama ventured into India with Indorama India Private Limited and set up its first plant in the country for manufacturing Spandex Filament at Baddi, Himachal Pradesh in 2012. The company produces a revolutionary spandex fibre under the brand name of INVIYA®, which is a versatile polymer yarn that can be readily used in conjunction with cotton or synthetic filament yarns to produce fashion apparel with unparalleled qualities and higher value additions. The state-of-art plant with

ISBIIPL A.I CyberSecurity Scoring

Scoring History

ISBIIPL

INVIYA Spandex Business | Indorama India Private Limited CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

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No data available

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A.I.

ISBIIPL Company Scoring based on AI Models

Cyber Incidents Likelihood 3 - 6 - 9 months

Actual

Prediction

Incident Predictions locked

Access Monitoring Plan

A.I Risk Score Likelihood 3 - 6 - 9 months

Actual

Prediction A

Prediction B

Prediction C

Prediction D

A.I. Risk Score Predictions locked

Access Monitoring Plan

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Underwriter Stats for ISBIIPL

Incidents vs Textile Manufacturing Industry Avg (This Year)

No incidents recorded for INVIYA Spandex Business | Indorama India Private Limited in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for INVIYA Spandex Business | Indorama India Private Limited in 2026.

Incident Types ISBIIPL vs Textile Manufacturing Industry Avg (This Year)

No incidents recorded for INVIYA Spandex Business | Indorama India Private Limited in 2026.

Incident History - ISBIIPL (X = Date, Y = Severity)

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SUBSIDIARY

INVIYA Spandex Business | Indorama India Private Limited Subsidiaries

Parent Company

ISBIIPL

Textile Manufacturing

Website: https://www.inviya.com

Company Size: 286

No subsidiary data available for this company.

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INVIYA Spandex Business | Indorama India Private Limited Similar Companies

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Shaw Industries

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Shaw Industries Group, Inc. is a leader in flooring and other surface solutions designed for residential housing, commercial spaces and outdoor environments. Leveraging strengths in design, innovation and operational excellence, the company takes a people-centered, customer-focused, and growth-minded approach to meet diverse market needs. It creates differentiated customer experiences through its expansive portfolio of brands: Anderson Tuftex, COREtec, Shaw Floors, Patcraft, Philadelphia Commercial, Shaw Contract, Shaw Sports Turf, Shawgrass, Southwest Greens, Watershed Geo and more. Headquartered in Dalton, Georgia, Shaw is a wholly owned subsidiary of Berkshire Hathaway, Inc. with approximately $6 billion in annual sales and 18,000 associates worldwide. Interested in a career with Shaw? Browse open positions at https://careers.shawinc.com/.

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NEWS

INVIYA Spandex Business | Indorama India Private Limited CyberSecurity News

Latest updates, reports, and threat intel affecting the global network.

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CVE

Latest

Global CVEs (Not Company-Specific)

CVE-2026-54236

SUMMARY

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm/entrypoints/anthropic/api_router.py (the POST /v1/messages and POST /v1/messages/count_tokens handlers), the Server-Sent Events streaming converter in vllm/entrypoints/anthropic/serving.py, and the realtime speech-to-text WebSocket in vllm/entrypoints/speech_to_text/realtime/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: 5.3)

cvss3

Base Score: 5.3

Complexity: LOW

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

Impact Score

1.4

Exploitability

3.9

REFERENCES

CVE-2026-54235

SUMMARY

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: )

cvss4

Base Score: 6.9

Complexity: LOW

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Impact Score

Exploitability

REFERENCES

CVE-2026-54233

SUMMARY

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to ~14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: 6.5)

cvss3

Base Score: 6.5

Complexity: LOW

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Impact Score

3.6

Exploitability

2.8

REFERENCES

CVE-2026-54232

SUMMARY

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: 8.8)

cvss3

Base Score: 8.8

Complexity: LOW

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Impact Score

5.9

Exploitability

2.8

REFERENCES

https://github.com/vllm-project/vllm/security/advisories/GHSA-jrf6-vqxq-pjv2

CVE-2026-53923

SUMMARY

vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: )

cvss4

Base Score: 5.3

Complexity: LOW

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Impact Score

Exploitability

REFERENCES

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